CTFR 18/349,355 CTFR 101532 DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The rejections from the Office Action of 1/22/2026 are hereby withdrawn. New grounds for rejection are presented below. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-10 were amended with Applicant’s response dated 4/20/2026. Claims 1-10 are rejected. Claim Objections 07-29-01 AIA Claim s 1, 9, and 10 are objected to because of the following informalities: The claims read “…processing the simplified profile using an artificial neural network is trained to detect anomalies…” but should read “…an artificial neural network trained…” Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. See MPEP 2106 for details. The following is the two-prong analysis for subject matter eligibility: Specifically, representative Claim 1 recites: The claim limitations in the abstract idea have been underlined below; the remaining limitations are “additional elements.” A method for detecting anomalies on a surface of an object, the method comprising: generating a depth profile of the surface of the object by measuring depth data of the surface of the object with a measurement system; generating a simplified profile from the depth profile by approximating an average shape of the object along a first spatial dimension of the object and determining the simplified profile by subtracting the average shape from the depth profile; and detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles. Similar limitations comprise the abstract ideas of Claims 9 and 10. Step 1: Claim 1 describes a method and falls under the four statutory categories. Claim 9 is a method claim and Claim 10 is a system. Step 2A - Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The claimed invention is directed to an abstract idea without significantly more. The underlined claim elements in Claim 1 above, namely “ generating a simplified profile by approximating an average shape of the object along a first spatial dimension of the object ” and “ determining the simplified profile by subtracting the average shape from the depth profile, ” as well as “ for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object ” in Claim 9 all recite both mental processes and mathematical calculations and are capable of being performed mentally or with the aid of pen and paper. For example, “approximating an average shape…along a…dimension” can be accomplished visually or graphically. Subtracting the average shape from the profile comprises two calculations: subtracting and averaging. Determining whether to discard an object based on detected anomalies is a judgement based on evidence and thus a mental process. Step 2A - Prong Two: Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. Claims 1, 9, and 10 do not amount to the recitation of a particular practical application as they do not recite any specific steps that would improve upon anomaly detection or discarding objects with detected anomalies. Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. In addition to the abstract ideas recited in Claims 1, 9, and 10, the claimed method and system recite the following additional elements: “ A method for detecting anomalies on a surface of an object, the method comprising: generating a depth profile of the surface of the object by measuring depth data of the surface of the object with a measurement system ” and “ detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles ,” in addition to “ A method for discarding objects of a plurality of objects, comprising: for each object of the plurality of objects, respectively detecting anomalies on a surface of the object in question ,” and “ for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded ” in Claim 9, and “ A system for detecting anomalies on the surface of an object, comprising a measurement system configured to generate a depth profile of the surface of the object ,” and “ a controller operably connected to the measurement system and configured to detect anomalies on the surface of the object, the controller configured to generate the depth profile of the surface of the object by operating the measurement system to measure depth data of the surface of the object ” in Claim 10. However, “…generating a depth profile…by measuring depth data…with a measurement system,” and “discarding the object…when it has been determined that the object in question is to be discarded” are both considered to be insignificant extra-solution activity. See MPEP 2106.05(g). Measuring the depth data is mere data gathering necessary to implement the abstract idea and further found to be well-understood, conventional, and routine in the art. Discarding the object after detecting the anomaly is stated in the Instant Specification to be well-understood, conventional, and routine. See paragraph [0003]-[0005] of the Specification: “in the context of quality assurance during a manufacturing process, objects or components are typically subjected to an inspection …it can be decided , for example, whether the object in question is to be readily further processed and used, or else scrapped or disposed of …” Detecting anomalies by use of a trained artificial neural network is recited with a high level of generality, with no limitations on how the trained ANN functions and thus amounts to no more than instructions to implement the ANN on a general use computer (See MPEP 2106.05(f)) and limit the limitation to the field of neural networks (See MPEP 2106.05(h)) . Similarly, the controller is recited so generically as to amount to no more than the recitation of a general use computer. See MPEP 2106.05(f). The measurement system is also generically recited and merely implements the generic data gathering addressed above. As such, it amounts to no more than an attempt to generally link the judicial exception to the technological environment of the measuring system. See MPEP 2106.05(h). Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claims 1, 9, and 10 amount to significantly more than the abstract idea. With regards to the dependent claims, Claims 2-8 , merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent Claim 1. Specifically: Claims 2 and 4 recite generating the simplified profile by applying a principal component analysis and polynomial approximation, respectively. Generation of the profile using either of these methods is within the abstract idea of claim one as the profile is the result of mathematical calculations. Similarly, Claim 3 merely provides further limitations on the PCA of Claim 2 and is thus part of the mathematical calculation. The simplified profile itself is merely a generic application and does not integrate the judicial exception into practical application or amount to significantly more. Claims 5-8 recite the controller configured to generate the depth profile, simplified profile, and detect anomalies. The controller has already been found to amount to no more than a general use computer. The subtraction of the average shape from the depth profile, PCA and polynomial approximation of Claims 6, 7, and 8 have already been shown to be mathematical calculations. As such, Claims 5, 6, and 7 do not introduce additional elements that integrate the claims into practical application or amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1, 2, 5, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. (US 20200166909 A1) in view of Mieno et. al. (US 20190162530 A1) . Regarding Claim 1 , Noone discloses a method for detecting anomalies on a surface of an object, the method comprising: generating a depth profile [ See Fig. [6b] ] of the surface of the object by measuring depth data of the surface of the object with a measurement system [ Paragraph [0007] – “In some embodiments, the one or more manufacturing process characterization sensors comprise at least one laser interferometer, machine vision system, or sensor that detects electromagnetic radiation that is reflected, scattered, absorbed, transmitted, or emitted by the object . In some embodiments, the one or more process control parameters are adjusted at a rate of at least 100 Hz.”; Paragraph [0009] – “In some embodiments, the manufactured object defects are detected and classified using…”; Paragraph [0017] – “ FIGS. 6A-B illustrate one non-limiting example of in-process feature monitoring using interferometry. FIG. 6A: schematic illustration of laser beams used to probe the geometry of the wire feed and melt pool overlaid with a photo of a laser-metal wire deposition process. FIG. 6B: cross-sectional profiles (i.e., height profiles across the width of the deposition) of the wire feed (solid line; peak) and previously deposited layer (solid line; shoulders) and resulting melt pool (dashed line). The x-axis (width) dimension is plotted in arbitrary units. The y-axis (height) dimension is plotted in units of millimeters relative to a fixed reference point below the deposition layer.”; See also Fig. [6b] ]. Noone does not disclose generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile. However, Mieno discloses generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile [ Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to subtract the average value of the shape of the object along a dimension from the depth profile, as disclosed by Mieno, using the depth data disclosed by Noone, in order to better approximate a simplified shape profile. The combination of Noone and Mieno discloses detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles [ Noone, Paragraph [0248] – “ Artificial neural networks (ANNs): In some cases, the machine learning algorithm used for the disclosed automated object defect classification or adaptive manufacturing process control methods may comprise an artificial neural network (ANN) , e.g., a deep machine learning algorithm… In some embodiments, the automated object defect classification and manufacturing process control methods and systems of the present disclosure may employ a pre-trained ANN architecture . In some embodiment, the automated object defect classification and additive manufacturing process control methods and systems of the present disclosure may employ an ANN architecture wherein the training data set is continuously updated …” ]. Regarding Claim 2 , the combination of Noone and Mieno discloses the method according to claim 1, the generating the simplified profile further comprising: applying a principal component analysis [ Noone, Paragraph [0247] – “Autoencoders are often used for the purpose of dimensionality reduction, i.e., the process of reducing the number of random variables under consideration by deducing a set of principal component variables. Dimensionality reduction may be performed, for example, for the purpose of feature selection (i.e., a subset of the original variables) or feature extraction (i.e., transformation of data in a high-dimensional space to a space of fewer dimensions).”; Paragraph [0260] – “For distributed systems, the sharing of data between one or more manufacturing apparatus, one or more process monitoring sensors, machine vision systems, and/or in-process inspection tools may be facilitated through the use of a data compression algorithm, a data feature extraction algorithm, or a data dimensionality reduction algorithm. ” ]. Regarding Claim 5 , the combination of Noone and Mieno discloses the method according to claim 1, wherein: a controller is configured to perform the method [ Noone, Paragraph [0265] – “The o ne or more processors, e.g., a CPU, execute a sequence of machine-readable instructions , which are embodied in a program (or software). The instructions are stored in a memory location. The instructions are directed to the CPU, which subsequently program or otherwise configure the CPU to implement the methods of the present disclosure . Examples of operations performed by the CPU include fetch, decode, execute, and write back . The CPU may be part of a circuit, such as an integrated circuit.” ], and the controller is configured to implement the generating the depth profile [ Noone, Paragraph [0230] – “Any of a variety of sensors, measurement tools, or inspection tools may be used for monitoring various manufacturing process parameters in real- time, including those listed above. In some embodiments, for example, laser interferometers are used to monitor the dimensions…or other part dimensions as the part is being fabricated. In some embodiments, the one or more sensors (e.g., image sensors, cameras, or machine vision systems) provide… In some embodiments, the one or more sensors may provide process characterization data to the processor programmed to run the machine learning algorithm may be updated at a rate of at least…” ], the generating the simplified profile [ Mieno, Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ], and the detecting the anomalies [ Noone, Paragraph [0262] – “ One or more processors may be employed to implement the machine learning algorithms, automated object defect classification methods, and manufacturing process control methods disclosed herein; Paragraph [0248] – “ Artificial neural networks (ANNs): In some cases, the machine learning algorithm used for the disclosed automated object defect classification or adaptive manufacturing process control methods may comprise an artificial neural network (ANN) , e.g., a deep machine learning algorithm…” ]. Regarding Claim 6 , the combination of Noone and Mieno discloses the method according to claim 5, wherein the controller is configured to apply a principal component analysis [ Noone, Paragraph [0247] – “Autoencoders are often used for the purpose of dimensionality reduction, i.e., the process of reducing the number of random variables under consideration by deducing a set of principal component variables. Dimensionality reduction may be performed, for example, for the purpose of feature selection (i.e., a subset of the original variables) or feature extraction (i.e., transformation of data in a high-dimensional space to a space of fewer dimensions).”; Paragraph [0260] – “For distributed systems, the sharing of data between one or more manufacturing apparatus, one or more process monitoring sensors, machine vision systems, and/or in-process inspection tools may be facilitated through the use of a data compression algorithm, a data feature extraction algorithm, or a data dimensionality reduction algorithm. ”; Paragraph [0267] – “ Some aspects of the methods and systems provided herein, such as the disclosed object defect classification or manufacturing process control algorithms, are implemented by way of machine (e.g., processor) executable code stored in an electronic storage location of the computer system, such as, for example, in the memory or electronic storage unit. The machine executable or machine readable code is provided in the form of software. During use, the code is executed by the one or more processors.” – the controller ] to generate the simplified profile [ Mieno, Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ]. Regarding Claim 10 , Noone discloses a system for detecting anomalies on a surface of an object, comprising: a measurement system configured to generate a depth profile of the surface of the object [ Paragraph [0275] – “…the expected outcome for an unsupervised machine learning process for classification of object defects . One or more automated inspection tools , e.g., machine vision systems coupled with automated image processing algorithms, are used to monitor and measure feature dimensions, angles, surface finishes, and/or other properties of fabricated parts…. Defects may be identified, e.g., by removing noise from the inspection data and subtracting a reference data set…and classified using an unsupervised machine learning algorithm …” ]; and a controller operably connected to the measurement system and configured to detect the anomalies on the surface of the object, the controller configured to: generate the depth profile of the surface of the object by operating the measurement system to measure depth data of the surface of the object [ Paragraph [0230] – “Any of a variety of sensors, measurement tools, or inspection tools may be used for monitoring various manufacturing process parameters in real- time, including those listed above. In some embodiments, for example, laser interferometers are used to monitor the dimensions…or other part dimensions as the part is being fabricated. In some embodiments, the one or more sensors (e.g., image sensors, cameras, or machine vision systems) provide… In some embodiments, the one or more sensors may provide process characterization data to the processor programmed to run the machine learning algorithm may be updated at a rate of at least…”; Paragraph [0265] – “ The one or more processors, e.g., a CPU, execute a sequence of machine-readable instructions , which are embodied in a program (or software). The instructions are stored in a memory location. The instructions are directed to the CPU, which subsequently program or otherwise configure the CPU to implement the methods of the present disclosure . Examples of operations performed by the CPU include fetch, decode, execute, and write back . The CPU may be part of a circuit, such as an integrated circuit.” ]. Noone does not disclose generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile. However, Mieno discloses generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile [ Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ]; It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to subtract the average value of the shape of the object along a dimension from the depth profile, as disclosed by Mieno, using the depth data disclosed by Noone, in order to better approximate a simplified shape profile. The combination of Noone and Mieno discloses detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles [ Noone, Paragraph [0248] – “ Artificial neural networks (ANNs): In some cases, the machine learning algorithm used for the disclosed automated object defect classification or adaptive manufacturing process control methods may comprise an artificial neural network (ANN) , e.g., a deep machine learning algorithm… In some embodiments, the automated object defect classification and manufacturing process control methods and systems of the present disclosure may employ a pre-trained ANN architecture . In some embodiment, the automated object defect classification and additive manufacturing process control methods and systems of the present disclosure may employ an ANN architecture wherein the training data set is continuously updated …” ] . 07-21-aia AIA Claim s 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. in view of Mieno et. al. , in further view of Turner et. al. (US 20220155694 A1) . Regarding Claim 3 , the combination of Noone and Mieno discloses the method according to claim 2, the generating the simplified profile further comprising: after subtracting the average shape from the depth profile [ Mieno, Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ] but fails to disclose further subtracting a plurality of principal components of the principal component analysis. The combination does not disclose further subtracting a plurality of principal components of the principal component analysis. However, Turner discloses further subtracting a plurality of principal components of the principal component analysis [ Paragraph [0094]-[0099] – “Further embodiments of the invention are disclosed in the list of numbered clauses below: 1. A method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising: obtaining inspection data comprising a plurality of sets of measurement data associated with a structure on the substrate or portion thereof; obtaining fingerprint data describing a spatial variation of a parameter of interest over a substrate or portion thereof; performing an iterative mapping of the inspection data to the fingerprint data ; and determining whether the substrate is subject to a process effect based on the degree to which the iterative mapping converges on a solution.”; Paragraph [0104]-[0105] – “6. A method according to any preceding clause, wherein said fingerprint data has been obtained from previous measurement data relating to at least one previous measurement of the known process effect. 7. A method according to clause 6, wherein said fingerprint data comprises a principal component of said previous measurement data .”; Paragraph [0112]-[0113] – “14. A method according to clause 13, wherein said correction for a wafer background fingerprint comprises performing a component analysis. 15. A method according to clause 14, wherein said correction for a wafer background fingerprint comprises removing one or more principal components other than the first principal component, when determining an updated fingerprint for each iteration of the iterative mapping. ” ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to repeat, as disclosed by Turner, the subtraction of principal components in the simplified profile of Noone and Mieno in order to further reduce data dimensionality and improve error detection. Regarding Claim 7 , the combination of Noone and Mieno discloses the method according to claim 6, wherein the controller is configured to execute various processes, after subtracting the average shape from the depth profile [ Mieno, Paragraph [0013] – “The present invention has been made to solve the above-described problem in the conventional technique, and a three-dimensional surface roughness evaluating device according to the present invention includes :”; Paragraph [0017] – “ a calculating device which generates three-dimensional surface roughness data of a measurement target on the basis of displacement data…”; Paragraph [0022] – “ generate three-dimensional surface roughness data of the measurement target by subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate. ”] – the surface evaluating device, which contains the calculating device, is the controller; Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ] but fails to disclose further subtracting a plurality of principal components of the principal component analysis. The combination does not disclose further subtracting a plurality of principal components of the principal component analysis. However, Turner discloses further subtracting a plurality of principal components of the principal component analysis [ Paragraph [0094]-[0099] – “Further embodiments of the invention are disclosed in the list of numbered clauses below: 1. A method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising: obtaining inspection data comprising a plurality of sets of measurement data associated with a structure on the substrate or portion thereof; obtaining fingerprint data describing a spatial variation of a parameter of interest over a substrate or portion thereof; performing an iterative mapping of the inspection data to the fingerprint data ; and determining whether the substrate is subject to a process effect based on the degree to which the iterative mapping converges on a solution.”; Paragraph [0104]-[0105] – “6. A method according to any preceding clause, wherein said fingerprint data has been obtained from previous measurement data relating to at least one previous measurement of the known process effect. 7. A method according to clause 6, wherein said fingerprint data comprises a principal component of said previous measurement data .”; Paragraph [0112]-[0113] – “14. A method according to clause 13, wherein said correction for a wafer background fingerprint comprises performing a component analysis. 15. A method according to clause 14, wherein said correction for a wafer background fingerprint comprises removing one or more principal components other than the first principal component, when determining an updated fingerprint for each iteration of the iterative mapping. ” ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to repeat, as disclosed by Turner, the subtraction of principal components in the simplified profile of Noone and Mieno, using the controller disclosed by Noone and Mieno, in order to further reduce data dimensionality and improve error detection . 07-21-aia AIA Claim s 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. in view of Mieno et. al. , in further view of Lukas-Simonyi et. al. (DE 3801297 A1) . Regarding Claim 4 , the combination of Noone and Mieno discloses the method according to claim 1. The combination does not disclose the generating the simplified profile further comprising: applying a polynomial approximation. However, Lukas-Simonyi discloses the generating the simplified profile further comprising: applying a polynomial approximation [ Paragraph [0002]-[0003] – “One example of such a stochastic measurement is the measured temperature of an engine blade during flight operation. In this process, the measured temperature values are plotted on a coordinate system as a function of time and represented as a stochastic measurement curve. Another application is when the surface microstructure of a workpiece needs to be recorded as accurately as possible , including any so-called [missing information] directed away from the surface… To assess roughness, that is, the surface texture of technical surfaces, roughness parameters are generally used, which are determined with stylus instruments.”; Paragraph [0022] – “ A method for measuring a stochastic quantity with a measuring device, of which a large number of measured values are acquired with a sensor and stored in a memory of the measuring device, wherein the successive measured values form a measurement curve in the form of a stochastic curve, wherein this curve is decomposed by means of a microprocessor and an algorithm into one or more long-wavelength components corresponding to the ripple and one or more short-wavelength, purely stochastic components, characterized in that the measurement curve is represented in the form of at least one trigonometric approximation or interpolation polynomial ….” – See also Claims 1-10, 15, and 16 ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply a polynomial approximation taught in Lukas-Simonyi to the simplified profiles of Noone and Mieno (see fig. [6B] of Noone) in order to better characterize the profiles. Regarding Claim 8 , the combination of Noone and Mieno discloses the method according to claim 5, wherein the controller is configured to execute various processes [ Noone, Paragraph [0247] – “Autoencoders are often used for the purpose of dimensionality reduction, i.e., the process of reducing the number of random variables under consideration by deducing a set of principal component variables. Dimensionality reduction may be performed, for example, for the purpose of feature selection (i.e., a subset of the original variables) or feature extraction (i.e., transformation of data in a high-dimensional space to a space of fewer dimensions).”; Paragraph [0260] – “For distributed systems, the sharing of data between one or more manufacturing apparatus, one or more process monitoring sensors, machine vision systems, and/or in-process inspection tools may be facilitated through the use of a data compression algorithm, a data feature extraction algorithm, or a data dimensionality reduction algorithm. ”; Paragraph [0267] – “ Some aspects of the methods and systems provided herein, such as the disclosed object defect classification or manufacturing process control algorithms, are implemented by way of machine (e.g., processor) executable code stored in an electronic storage location of the computer system, such as, for example, in the memory or electronic storage unit. The machine executable or machine readable code is provided in the form of software. During use, the code is executed by the one or more processors.” ]. The combination does not disclose applying a polynomial approximation to generate the simplified profile. However, Lukas-Simonyi discloses applying a polynomial approximation to generate the simplified profile [ Paragraph [0002]-[0003] – “One example of such a stochastic measurement is the measured temperature of an engine blade during flight operation. In this process, the measured temperature values are plotted on a coordinate system as a function of time and represented as a stochastic measurement curve. Another application is when the surface microstructure of a workpiece needs to be recorded as accurately as possible , including any so-called [missing information] directed away from the surface… To assess roughness, that is, the surface texture of technical surfaces, roughness parameters are generally used, which are determined with stylus instruments.”; Paragraph [0022] – “ A method for measuring a stochastic quantity with a measuring device, of which a large number of measured values are acquired with a sensor and stored in a memory of the measuring device, wherein the successive measured values form a measurement curve in the form of a stochastic curve, wherein this curve is decomposed by means of a microprocessor and an algorithm into one or more long-wavelength components corresponding to the ripple and one or more short-wavelength, purely stochastic components, characterized in that the measurement curve is represented in the form of at least one trigonometric approximation or interpolation polynomial ….” – See also Claims 1-10, 15, and 16 ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply a polynomial approximation taught in Lukas-Simonyi to the simplified profiles of Noone and Mieno (see fig. [6B] of Noone) in order to better characterize the profiles . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. (US 20200166909 A1) in view of Mieno et. al. (US 20190162530 A1), in further view of Fujiwara et. al. (JP 2021042988) . Regarding Claim 9 , Noone discloses a method for discarding objects of a plurality of objects, comprising: for each object of the plurality of objects, respectively detecting anomalies on a surface of the object in question by: generating a depth profile [ See Fig. [6b] ] of the surface of the object by measuring depth data of the surface of the object with a measurement system [ Paragraph [0007] – “In some embodiments, the one or more manufacturing process characterization sensors comprise at least one laser interferometer, machine vision system, or sensor that detects electromagnetic radiation that is reflected, scattered, absorbed, transmitted, or emitted by the object. In some embodiments, the one or more process control parameters are adjusted at a rate of at least 100 Hz.”; Paragraph [0009] – “In some embodiments, the manufactured object defects are detected and classified using…”; Paragraph [0017] – “FIGS. 6A-B illustrate one non-limiting example of in-process feature monitoring using interferometry. FIG. 6A: schematic illustration of laser beams used to probe the geometry of the wire feed and melt pool overlaid with a photo of a laser-metal wire deposition process. FIG. 6B: cross-sectional profiles (i.e., height profiles across the width of the deposition) of the wire feed (solid line; peak) and previously deposited layer (solid line; shoulders) and resulting melt pool (dashed line). The x-axis (width) dimension is plotted in arbitrary units. The y-axis (height) dimension is plotted in units of millimeters relative to a fixed reference point below the deposition layer.”; See also Fig. [6b] ]. Noone does not disclose generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile, and detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles. However, Mieno discloses generating a simplified profile from the depth profile by (i) approximating an average shape of the object along a first spatial dimension of the object and (ii) determining the simplified profile by subtracting the average shape from the depth profile [ Paragraph [0027]-[0028] – “ averaging the displacement data in the Y-axis direction and thus generating reference surface data of each coordinate; and subtracting the reference surface data of each coordinate from the displacement data of each X-Y plane coordinate, and thus generating three-dimensional surface roughness data of the measurement target .” ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to subtract the average value of the shape of the object along a dimension from the depth profile, as disclosed by Mieno, using the depth data disclosed by Noone, in order to better approximate a simplified shape profile. The combination of Noone and Mieno discloses detecting the anomalies on the surface of the object by processing the simplified profile using an artificial neural network is trained to detect anomalies in depth profiles [ Noone, Paragraph [0248] – “ Artificial neural networks (ANNs): In some cases, the machine learning algorithm used for the disclosed automated object defect classification or adaptive manufacturing process control methods may comprise an artificial neural network (ANN) , e.g., a deep machine learning algorithm… In some embodiments, the automated object defect classification and manufacturing process control methods and systems of the present disclosure may employ a pre-trained ANN architecture . In some embodiment, the automated object defect classification and additive manufacturing process control methods and systems of the present disclosure may employ an ANN architecture wherein the training data set is continuously updated …” ]. The combination of Noone and Mieno does not disclose for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object and for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded. However, Fujiwara discloses for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object [ Paragraph [0022] – “The object to be inspected is not particularly limited. In this embodiment, an industrial product such as a tire is used as the object to be inspected. In this case, the appearance inspection method of the first invention is performed on the production line of the industrial product. Then, a product determined to have an appearance defect is discarded without being shipped, or is shipped after the defect is repaired.” ]; and for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded [ Paragraph [0022] – “The object to be inspected is not particularly limited. In this embodiment, an industrial product such as a tire is used as the object to be inspected. In this case, the appearance inspection method of the first invention is performed on the production line of the industrial product. Then, a product determined to have an appearance defect is discarded without being shipped, or is shipped after the defect is repaired.” ]. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to detect anomalies on the plurality of objects and discard the object in question, as disclosed by Fujiwara, in the method of detecting anomalies disclosed by Noone and Mieno, in order to improve the quality of manufactured objects . Response to Arguments Applicant argues: PNG media_image1.png 496 644 media_image1.png Greyscale Examiner’s Response: Claim 5 has been amended to remove the use of the terms “provisioning unit,” “pre-processing unit,” and “detection unit” and is no longer being interpreted to invoke 35 U.S.C. 112(f). Applicant argues: PNG media_image2.png 610 648 media_image2.png Greyscale PNG media_image3.png 187 624 media_image3.png Greyscale Examiner’s Response: The Examiner agrees. New grounds of rejection are presented above. The artificial neural network of Claims 1, 9, and 10 is an additional element recited at such a high level of generality as to amount to the recitation of a component of a general-purpose computer with instructions to apply the mental processes/mathematical calculations of approximating, subtracting, and averaging and does not integrate the claims into practical application, nor amount to significantly more. As such, Claims 1, 9, 10, as well as dependent claims 2-8 are ineligible under 35 U.S.C. 101. Applicant Argues: PNG media_image4.png 857 654 media_image4.png Greyscale PNG media_image5.png 221 634 media_image5.png Greyscale Examiner’s Response: The Examiner disagrees. The artificial neural network of Claims 1, 9, and 10 is recited to merely implement the abstract idea/mental process of detecting anomalies, with no recitation of how the artificial neural network functions, is trained, or is improved in the claim. The Desjardins decision applies to improvements made to the claimed machine learning algorithm itself. See Ex parte Desjardins, “We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.” The specification recites that subtracting the averaged profile improves the detection of anomalies, but not how it improves the function of the ANN itself. Further, no processor is mentioned in Claims 1 and 9 that would be improved by the subtraction of the averaged profile prior to using the ANN. In the case of Claim 10, the presence of the controller merely serves as a general-purpose computer upon which the subtraction of the averaged profile and subsequent detection of anomalies are executed and the abstract ideas are not integrated into practical application, nor do they amount to significantly more. As such, Claims 1, 9, 10, as well as dependent claims 2-8 are ineligible under 35 U.S.C. 101. Applicant argues: PNG media_image6.png 296 653 media_image6.png Greyscale PNG media_image7.png 333 634 media_image7.png Greyscale PNG media_image8.png 139 634 media_image8.png Greyscale PNG media_image9.png 290 638 media_image9.png Greyscale Examiner’s Response: The Examiner agrees with Applicant’s arguments with respect to Claims 1, 2, 5, 6, and 10. The 35 U.S.C 102 Rejection has been withdrawn and new grounds for rejection are presented above. Applicant argues: PNG media_image10.png 836 647 media_image10.png Greyscale PNG media_image11.png 622 628 media_image11.png Greyscale Examiner’s Response: The Examiner agrees with Applicant’s arguments with respect to the 35 U.S.C 103 Rejection of Claims 3, 4, 7, 8, and 9. The rejection has been withdrawn and new grounds for rejection in light of the new combination of references are presented above. Pertinent Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20210027442 A1, Price, R., System and method for Automated Surface Assessment, 2021 . Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANELLE A HOLMES whose telephone number is (571)272-4336. The examiner can normally be reached Monday - Friday 8:00 am - 5 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /J.A.H./Examiner, Art Unit 2857 /ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857 Application/Control Number: 18/349,355 Page 2 Art Unit: 2857 Application/Control Number: 18/349,355 Page 3 Art Unit: 2857 Application/Control Number: 18/349,355 Page 4 Art Unit: 2857 Application/Control Number: 18/349,355 Page 5 Art Unit: 2857 Application/Control Number: 18/349,355 Page 6 Art Unit: 2857 Application/Control Number: 18/349,355 Page 7 Art Unit: 2857 Application/Control Number: 18/349,355 Page 8 Art Unit: 2857 Application/Control Number: 18/349,355 Page 9 Art Unit: 2857 Application/Control Number: 18/349,355 Page 10 Art Unit: 2857 Application/Control Number: 18/349,355 Page 11 Art Unit: 2857 Application/Control Number: 18/349,355 Page 12 Art Unit: 2857 Application/Control Number: 18/349,355 Page 13 Art Unit: 2857 Application/Control Number: 18/349,355 Page 14 Art Unit: 2857 Application/Control Number: 18/349,355 Page 15 Art Unit: 2857 Application/Control Number: 18/349,355 Page 16 Art Unit: 2857 Application/Control Number: 18/349,355 Page 17 Art Unit: 2857 Application/Control Number: 18/349,355 Page 18 Art Unit: 2857 Application/Control Number: 18/349,355 Page 19 Art Unit: 2857 Application/Control Number: 18/349,355 Page 20 Art Unit: 2857 Application/Control Number: 18/349,355 Page 21 Art Unit: 2857 Application/Control Number: 18/349,355 Page 22 Art Unit: 2857 Application/Control Number: 18/349,355 Page 23 Art Unit: 2857 Application/Control Number: 18/349,355 Page 24 Art Unit: 2857 Application/Control Number: 18/349,355 Page 25 Art Unit: 2857 Application/Control Number: 18/349,355 Page 26 Art Unit: 2857
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